Return of grid seams: A superpixel algorithm using discontinuous multi-functional energy seam carving

Superpixels have been widely used for compact image representation in a large number of computer vision applications such as object recognition, segmentation, and depth estimation. Recently, a novel seam carving approach for superpixel generation called Grid Seams was introduced that significantly improved image structure preservation while maintaining global spatial constraints. While Grid Seams was able to achieve state-of-the-art superpixel accuracy, it only took advantage of rudimentary edge information in its energy function and as such did not account for other important image characteristics such as texture. Motivated by this, we present Return of Grid Seams (RGS), a novel extension of Grid Seams that takes advantage of not only structural variations, but also textural variations (in the form of texture distinctiveness) into a unified multi-functional energy along with global spatial constraints. Furthermore, RGS incorporates discontinuous seams in the optimization process to allow for greater flexibility in preserving structural information. Experimental results using the Berkeley Segmentation Dataset show that RGS is able to outperform Grid Seams as well as a number of other seam carving based superpixel methods.